Overview

Dataset statistics

Number of variables10
Number of observations193573
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.8 MiB
Average record size in memory80.0 B

Variable types

Numeric9
Categorical1

Alerts

carat is highly overall correlated with price and 3 other fieldsHigh correlation
cut is highly overall correlated with tableHigh correlation
price is highly overall correlated with carat and 3 other fieldsHigh correlation
table is highly overall correlated with cutHigh correlation
x is highly overall correlated with carat and 3 other fieldsHigh correlation
y is highly overall correlated with carat and 3 other fieldsHigh correlation
z is highly overall correlated with carat and 3 other fieldsHigh correlation

Reproduction

Analysis started2024-04-09 18:03:55.482654
Analysis finished2024-04-09 18:04:18.311958
Duration22.83 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

carat
Real number (ℝ)

HIGH CORRELATION 

Distinct248
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.79068785
Minimum0.2
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-04-09T23:34:18.497105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.3
Q10.4
median0.7
Q31.03
95-th percentile1.65
Maximum3.5
Range3.3
Interquartile range (IQR)0.63

Descriptive statistics

Standard deviation0.46268774
Coefficient of variation (CV)0.58517117
Kurtosis0.53739769
Mean0.79068785
Median Absolute Deviation (MAD)0.32
Skewness0.99513461
Sum153055.82
Variance0.21407994
MonotonicityNot monotonic
2024-04-09T23:34:18.804876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 10758
 
5.6%
1.01 10103
 
5.2%
0.31 9538
 
4.9%
0.7 7958
 
4.1%
0.32 7548
 
3.9%
0.9 6253
 
3.2%
0.41 5852
 
3.0%
0.71 5367
 
2.8%
1 5328
 
2.8%
0.4 4802
 
2.5%
Other values (238) 120066
62.0%
ValueCountFrequency (%)
0.2 30
 
< 0.1%
0.21 17
 
< 0.1%
0.22 3
 
< 0.1%
0.23 889
0.5%
0.24 809
0.4%
0.25 611
0.3%
0.26 722
0.4%
0.27 650
0.3%
0.28 466
0.2%
0.29 354
 
0.2%
ValueCountFrequency (%)
3.5 1
 
< 0.1%
3.4 1
 
< 0.1%
3.04 3
< 0.1%
3.01 7
< 0.1%
3 5
< 0.1%
2.74 3
< 0.1%
2.72 1
 
< 0.1%
2.71 1
 
< 0.1%
2.7 2
 
< 0.1%
2.66 3
< 0.1%

cut
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
5
92454 
4
49910 
3
37566 
2
11622 
1
 
2021

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters193573
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row5
4th row5
5th row4

Common Values

ValueCountFrequency (%)
5 92454
47.8%
4 49910
25.8%
3 37566
19.4%
2 11622
 
6.0%
1 2021
 
1.0%

Length

2024-04-09T23:34:19.062224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-09T23:34:19.289934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
5 92454
47.8%
4 49910
25.8%
3 37566
19.4%
2 11622
 
6.0%
1 2021
 
1.0%

Most occurring characters

ValueCountFrequency (%)
5 92454
47.8%
4 49910
25.8%
3 37566
19.4%
2 11622
 
6.0%
1 2021
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 193573
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 92454
47.8%
4 49910
25.8%
3 37566
19.4%
2 11622
 
6.0%
1 2021
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 193573
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 92454
47.8%
4 49910
25.8%
3 37566
19.4%
2 11622
 
6.0%
1 2021
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 193573
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 92454
47.8%
4 49910
25.8%
3 37566
19.4%
2 11622
 
6.0%
1 2021
 
1.0%

color
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5161567
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-04-09T23:34:19.482772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6230909
Coefficient of variation (CV)0.46160937
Kurtosis-0.81540761
Mean3.5161567
Median Absolute Deviation (MAD)1
Skewness0.15757838
Sum680633
Variance2.634424
MonotonicityNot monotonic
2024-04-09T23:34:19.667797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 44391
22.9%
2 35869
18.5%
3 34258
17.7%
5 30799
15.9%
1 24286
12.5%
6 17514
 
9.0%
7 6456
 
3.3%
ValueCountFrequency (%)
1 24286
12.5%
2 35869
18.5%
3 34258
17.7%
4 44391
22.9%
5 30799
15.9%
6 17514
 
9.0%
7 6456
 
3.3%
ValueCountFrequency (%)
7 6456
 
3.3%
6 17514
 
9.0%
5 30799
15.9%
4 44391
22.9%
3 34258
17.7%
2 35869
18.5%
1 24286
12.5%

clarity
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9750843
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-04-09T23:34:19.883809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5017758
Coefficient of variation (CV)0.37779721
Kurtosis-0.10738307
Mean3.9750843
Median Absolute Deviation (MAD)1
Skewness0.66410887
Sum769469
Variance2.2553305
MonotonicityNot monotonic
2024-04-09T23:34:20.111832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 53272
27.5%
4 48027
24.8%
5 30669
15.8%
2 30484
15.7%
6 15762
 
8.1%
7 10628
 
5.5%
8 4219
 
2.2%
1 512
 
0.3%
ValueCountFrequency (%)
1 512
 
0.3%
2 30484
15.7%
3 53272
27.5%
4 48027
24.8%
5 30669
15.8%
6 15762
 
8.1%
7 10628
 
5.5%
8 4219
 
2.2%
ValueCountFrequency (%)
8 4219
 
2.2%
7 10628
 
5.5%
6 15762
 
8.1%
5 30669
15.8%
4 48027
24.8%
3 53272
27.5%
2 30484
15.7%
1 512
 
0.3%

depth
Real number (ℝ)

Distinct153
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.820574
Minimum52.1
Maximum71.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-04-09T23:34:20.370847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum52.1
5-th percentile59.9
Q161.3
median61.9
Q362.4
95-th percentile63.5
Maximum71.6
Range19.5
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation1.0817044
Coefficient of variation (CV)0.017497482
Kurtosis2.4770411
Mean61.820574
Median Absolute Deviation (MAD)0.6
Skewness-0.27638236
Sum11966794
Variance1.1700843
MonotonicityNot monotonic
2024-04-09T23:34:20.705670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.9 10781
 
5.6%
62 10150
 
5.2%
61.8 9270
 
4.8%
62.1 8866
 
4.6%
61.6 8534
 
4.4%
62.2 8345
 
4.3%
62.3 7987
 
4.1%
61.7 7970
 
4.1%
62.4 7030
 
3.6%
61.5 6554
 
3.4%
Other values (143) 108086
55.8%
ValueCountFrequency (%)
52.1 1
 
< 0.1%
52.2 1
 
< 0.1%
52.7 1
 
< 0.1%
53.1 2
 
< 0.1%
53.2 2
 
< 0.1%
54.7 1
 
< 0.1%
54.9 2
 
< 0.1%
55 1
 
< 0.1%
55.1 1
 
< 0.1%
55.2 5
< 0.1%
ValueCountFrequency (%)
71.6 2
< 0.1%
70 1
 
< 0.1%
69.9 1
 
< 0.1%
69.6 1
 
< 0.1%
69.5 3
< 0.1%
69.4 1
 
< 0.1%
69.2 2
< 0.1%
69.1 1
 
< 0.1%
69 1
 
< 0.1%
68.9 1
 
< 0.1%

table
Real number (ℝ)

HIGH CORRELATION 

Distinct108
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.227675
Minimum49
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-04-09T23:34:20.996825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile54.2
Q156
median57
Q358
95-th percentile61
Maximum79
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9188443
Coefficient of variation (CV)0.033530006
Kurtosis0.81018001
Mean57.227675
Median Absolute Deviation (MAD)1
Skewness0.61906223
Sum11077733
Variance3.6819634
MonotonicityNot monotonic
2024-04-09T23:34:21.300110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 42194
21.8%
57 37827
19.5%
58 32045
16.6%
55 24429
12.6%
59 23784
12.3%
60 12584
 
6.5%
54 8281
 
4.3%
61 6002
 
3.1%
62 2545
 
1.3%
53 1069
 
0.6%
Other values (98) 2813
 
1.5%
ValueCountFrequency (%)
49 1
 
< 0.1%
51 6
 
< 0.1%
52 50
 
< 0.1%
53 1069
0.6%
53.1 1
 
< 0.1%
53.2 8
 
< 0.1%
53.3 17
 
< 0.1%
53.4 7
 
< 0.1%
53.5 10
 
< 0.1%
53.6 40
 
< 0.1%
ValueCountFrequency (%)
79 1
 
< 0.1%
76 1
 
< 0.1%
70 5
 
< 0.1%
69 10
 
< 0.1%
68 20
 
< 0.1%
67 32
 
< 0.1%
66 114
 
0.1%
65 157
0.1%
64.4 1
 
< 0.1%
64 376
0.2%

x
Real number (ℝ)

HIGH CORRELATION 

Distinct522
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7153121
Minimum0
Maximum9.65
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-04-09T23:34:21.574082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.29
Q14.7
median5.7
Q36.51
95-th percentile7.58
Maximum9.65
Range9.65
Interquartile range (IQR)1.81

Descriptive statistics

Standard deviation1.1094222
Coefficient of variation (CV)0.19411401
Kurtosis-0.80100603
Mean5.7153121
Median Absolute Deviation (MAD)0.92
Skewness0.36104978
Sum1106330.1
Variance1.2308175
MonotonicityNot monotonic
2024-04-09T23:34:21.881043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.32 2094
 
1.1%
4.34 2010
 
1.0%
4.38 1986
 
1.0%
4.37 1938
 
1.0%
4.33 1812
 
0.9%
4.31 1761
 
0.9%
4.35 1685
 
0.9%
4.41 1644
 
0.8%
4.36 1568
 
0.8%
4.3 1558
 
0.8%
Other values (512) 175517
90.7%
ValueCountFrequency (%)
0 3
 
< 0.1%
3.75 2
 
< 0.1%
3.77 7
< 0.1%
3.78 5
< 0.1%
3.79 4
< 0.1%
3.81 7
< 0.1%
3.82 6
< 0.1%
3.84 9
< 0.1%
3.85 2
 
< 0.1%
3.86 9
< 0.1%
ValueCountFrequency (%)
9.65 1
 
< 0.1%
9.51 1
 
< 0.1%
9.46 1
 
< 0.1%
9.43 1
 
< 0.1%
9.42 4
< 0.1%
9.36 3
< 0.1%
9.35 1
 
< 0.1%
9.3 1
 
< 0.1%
9.24 1
 
< 0.1%
9.1 1
 
< 0.1%

y
Real number (ℝ)

HIGH CORRELATION 

Distinct521
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7200944
Minimum0
Maximum10.01
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-04-09T23:34:22.179372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.31
Q14.71
median5.72
Q36.51
95-th percentile7.58
Maximum10.01
Range10.01
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.1023335
Coefficient of variation (CV)0.19271246
Kurtosis-0.81066804
Mean5.7200944
Median Absolute Deviation (MAD)0.92
Skewness0.35675813
Sum1107255.8
Variance1.2151391
MonotonicityNot monotonic
2024-04-09T23:34:22.478614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.38 2163
 
1.1%
4.35 2088
 
1.1%
4.34 1986
 
1.0%
4.37 1911
 
1.0%
4.31 1854
 
1.0%
4.39 1719
 
0.9%
4.33 1711
 
0.9%
4.36 1625
 
0.8%
4.32 1618
 
0.8%
4.41 1593
 
0.8%
Other values (511) 175305
90.6%
ValueCountFrequency (%)
0 2
 
< 0.1%
3.71 1
 
< 0.1%
3.72 7
< 0.1%
3.73 1
 
< 0.1%
3.75 2
 
< 0.1%
3.77 4
< 0.1%
3.78 9
< 0.1%
3.79 2
 
< 0.1%
3.8 1
 
< 0.1%
3.81 5
< 0.1%
ValueCountFrequency (%)
10.01 1
< 0.1%
9.59 1
< 0.1%
9.46 1
< 0.1%
9.36 1
< 0.1%
9.34 1
< 0.1%
9.31 1
< 0.1%
9.3 2
< 0.1%
9.26 2
< 0.1%
9.24 2
< 0.1%
9.14 1
< 0.1%

z
Real number (ℝ)

HIGH CORRELATION 

Distinct349
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5342463
Minimum0
Maximum31.3
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-04-09T23:34:22.740285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.66
Q12.9
median3.53
Q34.03
95-th percentile4.684
Maximum31.3
Range31.3
Interquartile range (IQR)1.13

Descriptive statistics

Standard deviation0.68892211
Coefficient of variation (CV)0.19492759
Kurtosis12.818313
Mean3.5342463
Median Absolute Deviation (MAD)0.57
Skewness0.68567148
Sum684134.67
Variance0.47461367
MonotonicityNot monotonic
2024-04-09T23:34:23.027983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.69 3523
 
1.8%
2.7 3305
 
1.7%
2.68 3289
 
1.7%
2.71 3087
 
1.6%
2.72 2887
 
1.5%
2.73 2747
 
1.4%
2.67 2628
 
1.4%
3.99 2542
 
1.3%
4.02 2430
 
1.3%
4.01 2384
 
1.2%
Other values (339) 164751
85.1%
ValueCountFrequency (%)
0 10
< 0.1%
1.05 1
 
< 0.1%
2.24 2
 
< 0.1%
2.26 1
 
< 0.1%
2.27 2
 
< 0.1%
2.28 1
 
< 0.1%
2.3 4
 
< 0.1%
2.31 9
< 0.1%
2.32 5
< 0.1%
2.33 7
< 0.1%
ValueCountFrequency (%)
31.3 1
< 0.1%
8.4 1
< 0.1%
8.35 1
< 0.1%
8.18 1
< 0.1%
6.03 1
< 0.1%
5.75 1
< 0.1%
5.73 1
< 0.1%
5.69 1
< 0.1%
5.67 2
< 0.1%
5.65 1
< 0.1%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct8738
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3969.1554
Minimum326
Maximum18818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-04-09T23:34:23.280057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum326
5-th percentile544
Q1951
median2401
Q35408
95-th percentile13298
Maximum18818
Range18492
Interquartile range (IQR)4457

Descriptive statistics

Standard deviation4034.3741
Coefficient of variation (CV)1.0164314
Kurtosis2.106914
Mean3969.1554
Median Absolute Deviation (MAD)1678
Skewness1.6055812
Sum7.6832132 × 108
Variance16276175
MonotonicityNot monotonic
2024-04-09T23:34:23.551473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
544 542
 
0.3%
605 464
 
0.2%
789 454
 
0.2%
828 438
 
0.2%
776 437
 
0.2%
802 435
 
0.2%
552 427
 
0.2%
561 416
 
0.2%
625 414
 
0.2%
596 401
 
0.2%
Other values (8728) 189145
97.7%
ValueCountFrequency (%)
326 15
< 0.1%
335 11
 
< 0.1%
336 11
 
< 0.1%
337 5
 
< 0.1%
338 11
 
< 0.1%
344 5
 
< 0.1%
345 15
< 0.1%
348 5
 
< 0.1%
351 25
< 0.1%
357 31
< 0.1%
ValueCountFrequency (%)
18818 6
 
< 0.1%
18804 9
< 0.1%
18795 10
< 0.1%
18791 15
< 0.1%
18787 5
 
< 0.1%
18780 6
 
< 0.1%
18766 12
< 0.1%
18760 6
 
< 0.1%
18759 9
< 0.1%
18757 8
< 0.1%

Interactions

2024-04-09T23:34:15.014019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:33:58.903126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:00.889519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:02.813771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:05.057535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:07.104902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:09.099554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:11.117029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:13.031163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:15.249040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:33:59.134657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:01.129369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:03.029377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:05.268545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:07.319922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:09.331841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:11.317031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:13.242180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:15.496050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:33:59.349456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:01.330378image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:03.262404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:05.512081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:07.558045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:09.537844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:11.514859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:13.464793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:15.715077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:33:59.567466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:01.546587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:03.468908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:05.740564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:07.811954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:09.768685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:11.761535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:13.678830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:15.977093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:33:59.793235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:01.777313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:03.696936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:05.974578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:08.039336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:10.016719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:11.990820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:13.932829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:16.207837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:00.001295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:01.979733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:03.902951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:06.192955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:08.240226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:10.212514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:12.206858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:14.156312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:16.709823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:00.226314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:02.178321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:04.143445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:06.437027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:08.432247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:10.471516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:12.408687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:14.386183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:16.944500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:00.430345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:02.375338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:04.343465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:06.650228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:08.666323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:10.666553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:12.617217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:14.601472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:17.170336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:00.663345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:02.606601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:04.595891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:06.861243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:08.890339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:10.897784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:12.815242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-09T23:34:14.808005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-04-09T23:34:23.754514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
caratclaritycolorcutdepthpricetablexyz
carat1.000-0.3670.2460.1310.0420.9710.2360.9970.9960.995
clarity-0.3671.0000.0910.115-0.080-0.245-0.182-0.365-0.362-0.369
color0.2460.0911.0000.0410.0410.1730.0380.2430.2440.247
cut0.1310.1150.0411.000-0.215-0.140-0.558-0.150-0.149-0.173
depth0.042-0.0800.041-0.2151.0000.030-0.155-0.007-0.0080.110
price0.971-0.2450.173-0.1400.0301.0000.2230.9720.9720.968
table0.236-0.1820.038-0.558-0.1550.2231.0000.2400.2340.211
x0.997-0.3650.243-0.150-0.0070.9720.2401.0000.9980.990
y0.996-0.3620.244-0.149-0.0080.9720.2340.9981.0000.990
z0.995-0.3690.247-0.1730.1100.9680.2110.9900.9901.000

Missing values

2024-04-09T23:34:17.445354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-09T23:34:17.882927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

caratcutcolorclaritydepthtablexyzprice
01.5243462.258.07.277.334.5513619
12.0337262.058.08.068.125.0513387
20.7054561.257.05.695.733.502772
30.3254561.656.04.384.412.71666
41.7044462.659.07.657.614.7714453
51.5137362.858.07.347.294.597506
60.7452461.857.05.765.793.573229
71.3444262.557.07.007.054.386224
80.3053862.056.04.354.372.70886
90.3027563.657.04.264.282.72421
caratcutcolorclaritydepthtablexyzprice
1935630.2832361.255.04.234.262.60484
1935640.9021363.257.06.116.143.884919
1935650.3141362.658.04.324.292.69732
1935661.0554562.156.06.546.514.067397
1935670.5852461.857.05.335.363.311872
1935680.3151661.156.04.354.392.671130
1935690.7044660.358.05.755.773.472874
1935700.7333363.157.05.725.753.623036
1935710.3431362.955.04.454.492.81681
1935720.7122260.864.05.735.713.482258